Practical Strategies for Improving Men’s Health: Maximizing the Patient-Provider Encounter
Bibliographic record
Abstract
An inconsistent or lack of access to a healthcare provider (HCP) can lead to advanced morbidity and is an oft-cited barrier to advancing health, particularly in the U.S. Review of select literature consistently suggests men are far less likely to engage within the healthcare system, which is particularly problematic relating to preventive service access. As many health conditions are preventable and/or treatable in earlier stages, delay in screening and treatment often leads to long-term adverse health outcomes. Lack of early and frequent preventive healthcare may even be perceived as “normative” where poorer health outcomes in males are expected. Some evidence demonstrates a clear connection that seeking help via healthcare runs contrary to masculinity and dominant masculine principles, such as being strong/sturdy, working through pain, avoiding weakness, and/or perceptions of femininity, among other psychosocial phenomena. Changing healthcare “culture” concerning the care of males (i.e., gender-sensitive care) may provide a salient avenue to encourage more consistent and preventive contact, or “touch points,” in the patient-provider dynamic. There is a need to understand how social norms and practices in healthcare and medical settings can be effectively leveraged to address life-long male health outcomes versus focusing on late(r)-stage palliative care. The purpose of this article is to advance dialogue concerning practical considerations, such as resources (e.g. time, money) and methods (e.g., practitioners considering whether men respond best to immediate efforts to establish rapport versus a traditional power-based dynamic during the medical interaction) to inform gender-sensitive touchpoints in the healthcare of men. Location and types of facilities where men are willing to seek care (preventative or palliative) also need to be considered in a holistic, gendersensitive patient-provider healthcare model. Implications, policies, and evidence-based practical strategies for leveraging medical education, prevention programming, proper and improper recognition, and health management, and long-term treatment are presented and discussed with the practitioner in mind. Although there is a U.S.-focus with our proposed strategies, we aim to provide a more global context with our future work on this topic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".